from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

!pip install h2o

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting h2o
  Downloading h2o-3.40.0.2.tar.gz (177.6 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 177.6/177.6 MB 4.9 MB/s eta 0:00:00
  Preparing metadata (setup.py) ... done
Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from h2o) (2.25.1)
Requirement already satisfied: tabulate in /usr/local/lib/python3.9/dist-packages (from h2o) (0.8.10)
Requirement already satisfied: future in /usr/local/lib/python3.9/dist-packages (from h2o) (0.16.0)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->h2o) (2.10)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->h2o) (2022.12.7)
Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.9/dist-packages (from requests->h2o) (4.0.0)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->h2o) (1.26.15)
Building wheels for collected packages: h2o
  Building wheel for h2o (setup.py) ... done
  Created wheel for h2o: filename=h2o-3.40.0.2-py2.py3-none-any.whl size=177693439 sha256=c889ea192bd3ffe65aff7da84c509223a41d1df2158b3e6621b0639ec5aa4c63
  Stored in directory: /root/.cache/pip/wheels/b2/79/e3/842b81607eb31946ee24898cc9961b101e6486f988a5103967
Successfully built h2o
Installing collected packages: h2o
Successfully installed h2o-3.40.0.2

import h2o
from h2o.automl import H2OAutoML

h2o.init()

# Import data
f = "/content/drive/MyDrive/Qualities in Intelligent Systems/Bike-Sharing-Dataset/day.csv"
df = h2o.import_file(f)

# Reponse column
y = "cnt"

# Split into train & test
splits = df.split_frame(ratios = [0.8], seed = 1)
train = splits[0]
test = splits[1]

# Run AutoML for 1 minute
aml = H2OAutoML(max_runtime_secs=60, seed=1)
aml.train(y=y, training_frame=train)

# Explain leader model & compare with all AutoML models
exa = aml.explain(test)

# Explain a single H2O model (e.g. leader model from AutoML)
exm = aml.leader.explain(test)

Checking whether there is an H2O instance running at http://localhost:54321..... not found.
Attempting to start a local H2O server...
  Java Version: openjdk version "11.0.18" 2023-01-17; OpenJDK Runtime Environment (build 11.0.18+10-post-Ubuntu-0ubuntu120.04.1); OpenJDK 64-Bit Server VM (build 11.0.18+10-post-Ubuntu-0ubuntu120.04.1, mixed mode, sharing)
  Starting server from /usr/local/lib/python3.9/dist-packages/h2o/backend/bin/h2o.jar
  Ice root: /tmp/tmp7fslgtys
  JVM stdout: /tmp/tmp7fslgtys/h2o_unknownUser_started_from_python.out
  JVM stderr: /tmp/tmp7fslgtys/h2o_unknownUser_started_from_python.err
  Server is running at http://127.0.0.1:54321
Connecting to H2O server at http://127.0.0.1:54321 ... successful.

H2O_cluster_uptime:	02 secs
H2O_cluster_timezone:	Etc/UTC
H2O_data_parsing_timezone:	UTC
H2O_cluster_version:	3.40.0.2
H2O_cluster_version_age:	7 days, 23 hours and 54 minutes
H2O_cluster_name:	H2O_from_python_unknownUser_kavmgw
H2O_cluster_total_nodes:	1
H2O_cluster_free_memory:	3.172 Gb
H2O_cluster_total_cores:	2
H2O_cluster_allowed_cores:	2
H2O_cluster_status:	locked, healthy
H2O_connection_url:	http://127.0.0.1:54321
H2O_connection_proxy:	{"http": null, "https": null}
H2O_internal_security:	False
Python_version:	3.9.16 final

Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
AutoML progress: |███████████████████████████████████████████████████████████████| (done) 100%

Leaderboard

Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default.

model_id	rmse	mse	mae	rmsle	mean_residual_deviance	training_time_ms	predict_time_per_row_ms	algo
StackedEnsemble_BestOfFamily_3_AutoML_1_20230317_151130	98.106	9624.79	67.2775	0.0329657	9624.79	240	0.232783	StackedEnsemble
StackedEnsemble_BestOfFamily_2_AutoML_1_20230317_151130	98.106	9624.79	67.2775	0.0329657	9624.79	288	0.069139	StackedEnsemble
StackedEnsemble_AllModels_1_AutoML_1_20230317_151130	99.3945	9879.27	67.246	0.0311356	9879.27	566	0.18038	StackedEnsemble
StackedEnsemble_AllModels_2_AutoML_1_20230317_151130	99.9931	9998.62	67.6545	0.0312209	9998.62	283	0.167626	StackedEnsemble
GBM_3_AutoML_1_20230317_151130	103.66	10745.5	69.3182	0.0348153	10745.5	642	0.034323	GBM
GBM_4_AutoML_1_20230317_151130	143.498	20591.7	95.6842	0.0507063	20591.7	993	0.044029	GBM
StackedEnsemble_BestOfFamily_1_AutoML_1_20230317_151130	158.688	25182	107.911	0.0539144	25182	774	0.044236	StackedEnsemble
XGBoost_1_AutoML_1_20230317_151130	166.693	27786.6	117.504	0.0562133	27786.6	618	0.014036	XGBoost
GBM_2_AutoML_1_20230317_151130	177.254	31418.8	120.446	0.0624025	31418.8	1162	0.027619	GBM
XGBoost_grid_1_AutoML_1_20230317_151130_model_1	192.431	37029.7	147.774	0.0930426	37029.7	251	0.013005	XGBoost
GBM_5_AutoML_1_20230317_151130	202.543	41023.8	131.344	0.070276	41023.8	324	0.023635	GBM
XGBoost_2_AutoML_1_20230317_151130	206.521	42651	155.068	0.0683256	42651	659	0.012363	XGBoost
XGBoost_grid_1_AutoML_1_20230317_151130_model_2	217.912	47485.4	168.958	0.0660485	47485.4	109	0.010368	XGBoost
DRF_1_AutoML_1_20230317_151130	250.339	62669.4	176.565	0.0969177	62669.4	801	0.01938	DRF
XRT_1_AutoML_1_20230317_151130	298.545	89129.4	203.117	0.111638	89129.4	300	0.008238	DRF
GBM_1_AutoML_1_20230317_151130	318.378	101364	243.11	0.135626	101364	1574	0.020177	GBM
GLM_1_AutoML_1_20230317_151130	344.39	118604	257.38	0.129314	118604	100	0.004387	GLM
GBM_grid_1_AutoML_1_20230317_151130_model_1	380.558	144824	260.046	0.141757	144824	251	0.019313	GBM
DeepLearning_1_AutoML_1_20230317_151130	396.334	157080	292.219	0.157206	157080	106	0.010369	DeepLearning
XGBoost_3_AutoML_1_20230317_151130	1228.5	1.50921e+06	1087.23	0.302479	1.50921e+06	687	0.005119	XGBoost

[20 rows x 9 columns]

Residual Analysis

Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see "striped" lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable.

Learning Curve Plot

Learning curve plot shows the loss function/metric dependent on number of iterations or trees for tree-based algorithms. This plot can be useful for determining whether the model overfits.

Variable Importance

The variable importance plot shows the relative importance of the most important variables in the model.

Variable Importance Heatmap

Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity.

Model Correlation

This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text.

SHAP Summary

SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

Individual Conditional Expectation

An Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction.

Residual Analysis

Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see "striped" lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable.

Learning Curve Plot

Learning curve plot shows the loss function/metric dependent on number of iterations or trees for tree-based algorithms. This plot can be useful for determining whether the model overfits.

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

Individual Conditional Expectation

An Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction.

import h2o
from h2o.automl import H2OAutoML

h2o.init()

# Import data
f = "/content/drive/MyDrive/Qualities in Intelligent Systems/Bike-Sharing-Dataset/hour.csv"
df = h2o.import_file(f)

# Reponse column
y = "cnt"

# Split into train & test
splits = df.split_frame(ratios = [0.8], seed = 1)
train = splits[0]
test = splits[1]

# Run AutoML for 1 minute
aml = H2OAutoML(max_runtime_secs=60, seed=1)
aml.train(y=y, training_frame=train)

# Explain leader model & compare with all AutoML models
exa = aml.explain(test)

# Explain a single H2O model (e.g. leader model from AutoML)
exm = aml.leader.explain(test)

Checking whether there is an H2O instance running at http://localhost:54321. connected.

H2O_cluster_uptime:	6 mins 26 secs
H2O_cluster_timezone:	Etc/UTC
H2O_data_parsing_timezone:	UTC
H2O_cluster_version:	3.40.0.2
H2O_cluster_version_age:	8 days
H2O_cluster_name:	H2O_from_python_unknownUser_kavmgw
H2O_cluster_total_nodes:	1
H2O_cluster_free_memory:	2.965 Gb
H2O_cluster_total_cores:	2
H2O_cluster_allowed_cores:	2
H2O_cluster_status:	locked, healthy
H2O_connection_url:	http://localhost:54321
H2O_connection_proxy:	{"http": null, "https": null}
H2O_internal_security:	False
Python_version:	3.9.16 final

Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
AutoML progress: |███████████████████████████████████████████████████████████████| (done) 100%

Leaderboard

Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default.

model_id	rmse	mse	mae	rmsle	mean_residual_deviance	training_time_ms	predict_time_per_row_ms	algo
StackedEnsemble_AllModels_1_AutoML_2_20230317_151751	2.11301	4.46482	1.68988	nan	4.46482	474	0.013401	StackedEnsemble
StackedEnsemble_BestOfFamily_2_AutoML_2_20230317_151751	2.11301	4.46482	1.68988	nan	4.46482	449	0.007542	StackedEnsemble
StackedEnsemble_BestOfFamily_1_AutoML_2_20230317_151751	2.11685	4.48106	1.69214	nan	4.48106	701	0.00787	StackedEnsemble
GLM_1_AutoML_2_20230317_151751	3.08055	9.48976	2.1927	nan	9.48976	289	0.00121	GLM
XGBoost_1_AutoML_2_20230317_151751	7.14944	51.1145	4.704	0.0928143	51.1145	2942	0.004964	XGBoost
GBM_1_AutoML_2_20230317_151751	12.3146	151.649	7.45037	0.149081	151.649	3393	0.01313	GBM
DRF_1_AutoML_2_20230317_151751	21.6649	469.367	10.9212	0.147284	469.367	441	0.001548	DRF
GBM_3_AutoML_2_20230317_151751	96.1387	9242.64	74.8388	1.19714	9242.64	300	0.002984	GBM
GBM_4_AutoML_2_20230317_151751	117.481	13801.8	91.5657	1.30082	13801.8	166	0.00264	GBM
GBM_2_AutoML_2_20230317_151751	128.581	16533	100.298	1.35206	16533	154	0.001923	GBM
XGBoost_3_AutoML_2_20230317_151751	180.168	32460.7	130.209	1.13754	32460.7	53	0.000492	XGBoost
XGBoost_2_AutoML_2_20230317_151751	180.685	32647.2	130.09	1.12879	32647.2	593	0.000833	XGBoost

[12 rows x 9 columns]

Residual Analysis

Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see "striped" lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable.

Learning Curve Plot

Learning curve plot shows the loss function/metric dependent on number of iterations or trees for tree-based algorithms. This plot can be useful for determining whether the model overfits.

Variable Importance

The variable importance plot shows the relative importance of the most important variables in the model.

Variable Importance Heatmap

Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity.

Model Correlation

This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text.

SHAP Summary

SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

Individual Conditional Expectation

An Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction.

Residual Analysis

Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see "striped" lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable.

Learning Curve Plot

Learning curve plot shows the loss function/metric dependent on number of iterations or trees for tree-based algorithms. This plot can be useful for determining whether the model overfits.

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

Individual Conditional Expectation

An Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction.

from sklearn.datasets import make_regression
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
from sklearn import metrics

# Import ddata
df=pd.read_csv("/content/drive/MyDrive/Qualities in Intelligent Systems/Bike-Sharing-Dataset/day.csv")

X = df.drop("cnt", axis=1)

y = df.pop("cnt")
X, y = make_regression(random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
reg = GradientBoostingRegressor(random_state=0)
reg.fit(X_train, y_train)
GradientBoostingRegressor(random_state=0)
reg.predict(X_test[1:2])
reg.score(X_test, y_test)

0.4403245677708285

To understand how various environmental conditions affect the number of bikes rented, we can create different plots to visualize the relationship between the input features and the target variable (i.e., the number of bikes rented).

1. Scatter Plots: A scatter plot can be used to visualize the relationship between two continuous variables, such as temperature and the number of bikes rented. We can plot temperature on the x-axis and the number of bikes rented on the y-axis. If there is a positive correlation between temperature and the number of bikes rented, we would expect to see a diagonal line moving from the lower left to the upper right of the plot.
2. Line Charts: A line chart can be used to visualize the trends of time-dependent variables, such as the day of the week or the month. We can plot the average number of bikes rented on the y-axis and time on the x-axis. This can help us to see if there are any patterns or trends in the data, such as more bikes being rented on weekends compared to weekdays.
3. Bar Charts: A bar chart can be used to visualize the relationship between a categorical variable and a continuous variable, such as weather conditions and the number of bikes rented. We can plot the average number of bikes rented on the y-axis and weather conditions on the x-axis. This can help us to see if there are any differences in the number of bikes rented on different weather conditions.
4. Heatmaps: A heatmap can be used to visualize the relationship between two categorical variables, such as month and weather conditions. We can plot the average number of bikes rented on the y-axis, month on the x-axis, and use a color scale to represent the number of bikes rented for each weather condition. This can help us to see if there are any patterns or trends in the data, such as more bikes being rented in the summer compared to the winter. By using these different types of plots, we can gain insights into how various environmental conditions affect the number of bikes rented. We can use these insights to make informed decisions on how to optimize bike rental services based on the current weather, time of the day, or season.